MICap: A Unified Model for Identity-aware Movie Descriptions (2405.11483v1)

Abstract: Characters are an important aspect of any storyline and identifying and including them in descriptions is necessary for story understanding. While previous work has largely ignored identity and generated captions with someone (anonymized names), recent work formulates id-aware captioning as a fill-in-the-blanks (FITB) task, where, given a caption with blanks, the goal is to predict person id labels. However, to predict captions with ids, a two-stage approach is required: first predict captions with someone, then fill in identities. In this work, we present a new single stage approach that can seamlessly switch between id-aware caption generation or FITB when given a caption with blanks. Our model, Movie-Identity Captioner (MICap), uses a shared auto-regressive decoder that benefits from training with FITB and full-caption generation objectives, while the encoder can benefit from or disregard captions with blanks as input. Another challenge with id-aware captioning is the lack of a metric to capture subtle differences between person ids. To this end, we introduce iSPICE, a caption evaluation metric that focuses on identity tuples created through intermediate scene graphs. We evaluate MICap on Large-Scale Movie Description Challenge (LSMDC), where we show a 4.2% improvement in FITB accuracy, and a 1-2% bump in classic captioning metrics.

• The paper introduces a unified single-stage model that integrates identity-aware captioning and fill-in-the-blanks tasks, achieving a 4.2% FITB accuracy gain and up to 1.8% METEOR improvement.
• It employs a Transformer-based encoder-decoder that fuses multimodal features, including semantic, action, and facial data processed with Arcface, to generate coherent captions.
• The unified approach enhances narrative comprehension in video descriptions and holds promise for improved accessibility services and advanced multimedia analysis.

Identity-aware Captioning for Movies: A Unified Approach

The paper "MICap: A Unified Model for Identity-aware Movie Descriptions" addresses a critical issue in video captioning—identifying and accurately describing the characters involved in a storyline. Previous work predominately focused on generating captions using placeholders for character names, presenting challenges for understanding narratives in a series of connected videos. This research proposes an innovative single-stage approach with their model, Movie-Identity Captioner (MICap), which unifies identity-aware caption generation and the fill-in-the-blanks (FITB) task, enhancing accuracy and coherence in narrative description.

Methodology and Approach

MICap improves upon prior methods by adopting a single-stage encoder-decoder framework that integrates the tasks of identity-aware captioning and FITB seamlessly. The model employs a shared auto-regressive decoder that capitalizes on both full-caption generation and FITB objectives. It transitions efficiently between generating captions with or without pre-specified identities (blanks) by exploiting a Transformer-based architecture.

The input representation is enriched with multimodal features including semantic, action, and facial data extracted from video clips. These diverse inputs are transformed into a unified latent space that feeds the Transformer encoder. By incorporating advanced face processing techniques, such as using Arcface, and attention mechanisms to aggregate spatial and identity-specific information across clips, MICap navigates the identity-aware captioning landscape with notable efficacy.

Results and Metrics

Evaluation on the Large-Scale Movie Description Challenge (LSMDC) dataset demonstrates the efficacy of MICap. It delivers superior performance, with a 4.2% enhancement in FITB accuracy and up to a 1.8% improvement in classic captioning metrics like METEOR. The introduction of iSPICE, an identity-sensitive extension of the SPICE metric, offers a novel way of evaluating captions that encapsulate identity-specific semantics, providing more nuanced insights into the model's performance on identity-aware tasks.

Implications and Future Directions

The MICap model presents strong implications for enhancing automated video description systems, particularly in accessibility-focused applications such as audio descriptions for the visually impaired. The ability to maintain character identity across scenes without relying solely on external databases broadens the applicability of this methodology to a wider range of video content, including independent films and personal video compilations.

Theoretically, the integration of identity information into the captioning pipeline enhances narrative comprehension, making it an important step towards developing more sophisticated AI capable of understanding complex stories. However, the model is currently limited to small groups of clips, which poses an exciting area for future work: scaling MICap to operate effectively across entire films. Additionally, exploring hybrid models that incorporate LLMs and external knowledge could further enhance narrative detail and accuracy.

Conclusion

Overall, MICap represents an advancement in identity-aware captioning by resolving the two-stage bottleneck of previous methodologies and providing a unified, efficient approach. The improvements seen in performance metrics attests to the potential of MICap in setting a new standard for video description tasks, and points to enriching the AI tools available for narrative-focused video content processing.